# statsmodels.regression.mixed_linear_model.MixedLM.fit¶

MixedLM.fit(start_params=None, reml=True, niter_sa=0, do_cg=True, fe_pen=None, cov_pen=None, free=None, full_output=False, method='bfgs', **kwargs)[source]

Fit a linear mixed model to the data.

Parameters: start_params (array-like or MixedLMParams) – Starting values for the profile log-likelihood. If not a MixedLMParams instance, this should be an array containing the packed parameters for the profile log-likelihood, including the fixed effects parameters. reml (bool) – If true, fit according to the REML likelihood, else fit the standard likelihood using ML. niter_sa – Currently this argument is ignored and has no effect on the results. cov_pen (CovariancePenalty object) – A penalty for the random effects covariance matrix do_cg (boolean, defaults to True) – If False, the optimization is skipped and a results object at the given (or default) starting values is returned. fe_pen (Penalty object) – A penalty on the fixed effects free (MixedLMParams object) – If not None, this is a mask that allows parameters to be held fixed at specified values. A 1 indicates that the correspondinig parameter is estimated, a 0 indicates that it is fixed at its starting value. Setting the cov_re component to the identity matrix fits a model with independent random effects. Note that some optimization methods do not respect this constraint (bfgs and lbfgs both work). full_output (bool) – If true, attach iteration history to results method (string) – Optimization method. A MixedLMResults instance.